Technology

𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀: 𝗧𝗵𝗲 𝗥𝗘𝗔𝗟 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗠𝗖𝗣. ⬇️ This image illustrates the difference with surprising clarity. Let’s break it down: 1. (𝗔𝗜) 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: 𝗙𝗼𝗹𝗹𝗼𝘄𝘀 𝗰𝗹𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ An AI workflow is like a recipe. It runs in a fixed order: An email arrives → the content is summarized → a task is created → the plan is sent via Slack. It’s linear, predictable, and doesn’t adapt. No decisions. No context-awareness. Just automation. 2. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗔𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵 𝗴𝗼𝗮𝗹𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 ➜ An AI agent doesn’t need step-by-step instructions. You give it a goal — for example, “Plan my day” — and it figures out how to get there. It accesses tools, checks your calendar, moves meetings, finds focus time, and adapts the schedule based on what matters. It makes decisions based on context — not just predefined logic. 3. 𝗠𝗖𝗣: 𝗘𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 ➜ The Model Context Protocol (MCP) is the key enabler. It gives the agent secure, real-time access to apps like Calendar, Notion, Slack, and Perplexity. This unlocks cross-app coordination, memory, and adaptive behavior. Not just running commands — but reasoning across systems. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝘁𝗲𝗽𝘀 ➜ 𝗔𝗴𝗲𝗻𝘁𝘀 𝗽𝘂𝗿𝘀𝘂𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 ➜ 𝗔𝗻𝗱 𝗠𝗖𝗣 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲!

When a data scientist looks at a pump, they see a dataset. When a maintenance technician looks at a dataset, they see gibberish. And therein lies the problem. 😮 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞? Predictive maintenance refers to the use of data analysis tools and techniques to detect anomalies in equipment and predict potential failures before they occur. This approach leverages data from sensors and machines to anticipate maintenance needs, thereby preventing costly downtime and extending the lifespan of machinery. The power of predictive maintenance lies in its ability to ensure operational efficiency and save substantial costs in the long run. By preventing unexpected equipment failures, companies can reduce downtime, enhance safety, and optimize spare parts handling, making operations smoother and more cost-effective. 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: 𝐓𝐰𝐨 𝐖𝐨𝐫𝐥𝐝𝐬 𝐂𝐨𝐥𝐥𝐢𝐝𝐢𝐧𝐠 However, integrating predictive maintenance into business operations isn't without its hurdles. One significant challenge is the cultural and knowledge gap between maintenance teams and AI experts. Maintenance professionals may lack a deep understanding of AI and data analytics, while AI specialists often do not possess firsthand knowledge of the intricate realities of day-to-day maintenance. This disparity can lead to miscommunication and inefficiencies in implementing predictive maintenance solutions. The companies that succeed in predictive maintenance are the ones that don’t just invest in technology—but also invest in breaking down silos between AI engineers and maintenance teams. 𝐀 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐭𝐡𝐞 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐡𝐢𝐧𝐝 𝐢𝐭. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 According to IoT Analytics, the predictive maintenance market is growing fast, hitting $𝟓.𝟓 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 in 2022 and is expected to grow by 𝟏𝟕% annually until 2028. The market has evolved to include three main types of predictive maintenance: indirect failure prediction, anomaly detection, and remaining useful life (RUL). Most companies adopting predictive maintenance report a positive ROI, with 𝟗𝟓% seeing benefits and 𝟐𝟕% recouping costs within a year. Successful vendors often specialize in specific industries or assets, and software tools in this space share common features like data collection, analytics, and third-party integration. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞, 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐦𝐚𝐠𝐞, 𝐚𝐧𝐝 𝐚𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: https://lnkd.in/erQ5HTab ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

As organizations increasingly adopt hybrid-cloud architectures, understanding the right path and tools is crucial for professionals aiming to deliver resilient, scalable, and efficient applications. Here’s a Cloud Native roadmap breaking down the skills and tools to master across critical domains. Dive in and explore the ecosystem that powers modern applications! 🔴 𝟭. 𝗟𝗶𝗻𝘂𝘅 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀   Linux remains at the heart of cloud-native systems. Get comfortable with terminal commands, bash scripting, and distributions like Ubuntu and Red Hat for a solid start. 🟢 𝟮. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀   Protocols like HTTP, SSL, and SSH form the backbone of connectivity. Tools like Wireshark are invaluable for monitoring and securing network traffic. 🔵 𝟯. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀   The cloud is non-negotiable! Whether AWS, Azure, or Google Cloud, understanding SaaS, PaaS, and IaaS is key to harnessing the cloud's potential. 🟣 𝟰. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆   Security is foundational in cloud-native environments. Tools like Open Policy Agent and Prisma provide the framework for enforcing policies and securing applications. 🟡 𝟱. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻   Containers revolutionized app deployment! Master Docker, Kubernetes, and service meshes like Istio to orchestrate, scale, and manage applications seamlessly. 🟠 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 (𝗜𝗮𝗖)   IaC tools like Terraform, Chef, and Puppet automate infrastructure, ensuring consistency and efficiency across deployments. IaC is a must for scalable cloud-native applications. 🟢 𝟳. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆   With tools like Prometheus, Grafana, and Elastic Stack, observability gives you the visibility needed to monitor, troubleshoot, and optimize performance in real time. 🔵 𝟴. 𝗖𝗜/𝗖𝗗   Continuous Integration and Delivery streamline deployments. GitLab, Jenkins, and GitOps practices (Argo) enable rapid, reliable application delivery. This roadmap covers essential areas for cloud-native development, from Linux fundamentals to CI/CD and observability. But, the cloud-native landscape is vast and rapidly evolving! Did I miss any critical tools or concepts? Whether it's a tool you swear by or an emerging trend you're excited about, drop it in the comments! 👇

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

During the ascent of #fintech as a disruption driver in #finance, digital banks have been the first and most impactful use case. Let’s take a look at their playbook. The term itself – alternatives include challenger banks or neobanks – characterizes players (usually new entrants) challenging the traditional banking model with a #technology-first approach that involves flexible, branchless, digital-native (mobile) banking, often focusing on or starting from niche segments and customers. An increasingly digital arena, a shift in consumer behaviour and a gap in product and customer focus by incumbents have enabled these new players to challenge the status quo. Their success and proliferation around the globe is a clear sign of agile, digital-first, product-niche strategies prevailing over traditional, monolithic, vertical banking #business models. Whereas different patterns can be identified in their evolutionary path, the successful models can be aggregated to two broad categories: — Greenfield players starting completely from scratch by means of identifying a niche market or segment, often neglected by incumbents, and focusing on seamless customer experience, attractive design, competitive pricing and a digital or mobile only set-up. In terms of strategy two elements clearly stand-out: 1) hyper-growth and scale as the core - sometimes only - metrics (which explains why so many have been unprofitable) 2) an ecosystem play, driven by horizontal partnerships (vs the vertical traditional model). N26, Revolut and Nubank are typical examples of this model. — Large, closed-loop ecosystem players with a non-finance business geared on technology and an anchor in #ecommerce launching (digital) #banking spin-offs as a means of converting (and monetizing) their existing client-base. Most (or almost all) of the examples here come from Asia (i.e. Webank, Kakaobank), mainly due to the set-up of the #economy (lacking a robust, finance architecture and, in effect, benefiting private, BigTech players covering the gap). Webank, for example, is owned by Tencent, China’s largest social-media BigTech company (owner of WeChat, China’s equivalent of Facebook). It has managed to reach a value of $33 billion and a base of more than 320 million active users by focusing on building a modern IT stack (as a competitive edge to traditional banks) and leveraging on the data generated by the Tencent ecosystem (i.e. retail lending credit scoring built on Tencent data, resulted in a non-performing loan ratio of just 1.2%, about half (or less) of the industry average for such non-secured loans). Irrespective of their origins, both models have been (fast) converging to what has become the new holy grail of modern finance: platform #economics and ecosystem plays. These are the concepts that will be defining the boundaries in an increasingly network and technology driven field. Opinions: my own, Graphic source: Momentum Works, Decoding digital banks

“Sorry, but your name has invalid characters.” 40 False Assumptions About Names In Interfaces (https://lnkd.in/esFQZBch) lists 40 wrong assumptions that usually result in poor error messages, lock-outs and dead ends. Key takeaways: ✅ People often have multiple full names. ✅ People don’t always have 1 full name which they go by. ✅ People’s names do change over time. ✅ There are dozens of various naming schemes worldwide. ✅ Allow people to type their name as they prefer. 🚫 Names aren’t always written in a single character set. 🚫 Don’t impose space or character limitations. 🚫 Not everyone has a last name, family name or middle name. 🚫 First names and last names aren’t always different. ✅ Names may include numbers and punctuation. ✅ Names also include prefixes, suffixes, everything-in-between. ✅ Systems often use different names for the same person. We shouldn’t make any assumptions about people’s names. There are literally dozens of different naming schemes around the world, and validating any names is usually a dangerous path to take. People whose names break validation aren’t outliers. They are real people with real names that don’t match our validation restrictions. The way out is easy: accept any name that a user provides, whatever characters they include, and whatever way they choose to type it. Useful articles: Creating More Inclusive and Culturally Sensitive Forms, by Mark H. Anbinder https://lnkd.in/eBBHuKyn How To Ask Users For Names, Gov.uk https://lnkd.in/eYTQutVh Designing Forms for Gender Diversity and Inclusion, by Sabrina Fonseca https://lnkd.in/e2UGu9N2 #ux #design

Story telling magic: In this immersive room you and your AI-adjusted shadow become part of a story of shadows. Whole space interacts with your movements and the light you carry.   A project by artist Joon Moon. On view at the G.MAP visual art center, Korea.   Here’s a longer description of the project:   “The viewer’s light moves the shade and shadows of an entire room that is an 8m x 8m x 4.2m immersive projection environment. It’s an interactive narrative in which the story progresses to next stages when the viewer finds and approaches the shadows who call him here and there. The total viewing time is about 14 minutes. The story was written so that all interfaces such as the viewer's light, the shadows, the optical illusion, and the immersive environment could be incorporated into the narrative. It intended the viewer to be engaged completely in the narrative.   The room was installed without any openings not only for surround projection, but also for the beginning of the story. Shadow kids are stuck in this room. They are flat shadows, but they become three-dimensional as the story progresses. Fishes jump out of the plane of the floor and swim in the air. In this optical illusion, the viewer deeply engages with the kids who make eye contact and take a light from the viewer’s hand. Using the light, the kids draw new shades and three-dimensional space. Doors open on the wall, and the kids explore the space hidden behind the room. Between 2D and 3D, they draw a pleasant story that the optical illusion creates.”   I’ve added the website of Joon Moon in the comments.

DevOps in 2025: Winning Skills and Real Trends Two years ago, DevOps was a high-demand field. In 2025, it’s the backbone of every digital transformation—supercharged by cloud, automation, and now, AI. Here's what caught my attention 👇 📈 DevOps market is projected to expand from $13.2 billion in 2024 to an impressive $81.1 billion by 2028 📈 From specialized approach to mainstream strategy: Its adoption soared from 33% of companies in 2017 to an estimated 80% in 2024. Let me break down what's really happening out there and how you can ride this wave—whether you're just starting or gunning for that architect role. 📊 𝗪𝗵𝗶𝗰𝗵 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲 𝗝𝗼𝗯 𝗣𝗼𝘀𝘁𝗶𝗻𝗴𝘀? Based on aggregated data from 2024-2025 DevOps job postings, here’s the tech that consistently tops job requirements: 1 - Terraform 88% (+9%) 2 - Python 80% (+8%) 3 - Kubernetes 76% (+6%) 4 - AWS 72% (–3%) 5 - Jenkins 74% (+6%) 6 - Docker 68% (+3%) 7 - Azure 60% (+6%) 8 - Git/GitHub 60% (+2%) .... 19 - Golang 18% (+13%) The pattern is clear: Infrastructure as Code is king, container orchestration is everywhere, and you better know your way around multiple clouds. Golang is the surprise breakout. 🌐 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗛𝘂𝗻𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 ↳ Cloud-native expertise is “non-negotiable”: 83% of organizations now use multi-cloud approaches. If you can juggle AWS, Azure, AND Kubernetes? You're golden. ↳ Architects and senior engineers who bridge DevOps, cloud, and AI lead the next evolution. These are the people building scalable, secure, AI-ready infrastructure—roles that are multiplying fast. ↳ Platform engineering is having a moment: Everyone wants internal platforms that make their developers' lives easier. 🤖 𝗔𝗜 𝗜𝘀𝗻'𝘁 𝗞𝗶𝗹𝗹𝗶𝗻𝗴 𝗗𝗲𝘃𝗢𝗽𝘀 (𝗜𝘁'𝘀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗜𝘁 𝗕𝗲𝘁𝘁𝗲𝗿) ✅ AI/ML is making DevOps smarter—think smart incident response, predictive analytics, and self-healing infrastructure that fixes itself. ⚙️ But success still comes down to knowing your foundations: DevOps, cloud architecture, and scripting. 🚦 𝗖𝗮𝗿𝗲𝗲𝗿 𝗔𝗱𝘃𝗶𝗰𝗲: 𝗖𝗵𝗼𝗼𝘀𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵, 𝗧𝗵𝗲𝗻 𝗚𝗼 𝗗𝗲𝗲𝗽 - Get dangerous with 2 automation tools (Terraform + K8s is the combo right now) - Go deep with AWS or Azure, but stay curious about the others - Python is your Swiss Army knife—learn it, love it - Don't sleep on AI tools, but master your CI/CD and container game first 🎯 𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗰𝗮𝗿𝗲𝗲𝗿 𝗿𝗼𝗮𝗱𝗺𝗮𝗽? We’ve compiled all the proven insights—plus actual salary data, skills breakdowns, and stepwise growth plans—into the latest DevOps Career Guide. 📌 Grab it here: https://bit.ly/44TevO0 💬 What are you seeing in your corner of the DevOps world? What skills are you stacking for 2025? Sources: - Forrester: DevOps and Platform Engineering, 2025 - DevOpsCube Report, 2025 - Prepare.sh: DevOps Job Market Trends 2025

“DOE expects a surge in annual DER additions from 2025 to 2030, including 20 GW to 90 GW of demand capacity from EV charging infrastructure and 300 GWh to 540 GWh of storage capacity from EV batteries. It expects smart thermostats, smart water heaters and non-residential DER will contribute an additional 5 GW to 6 GW of flexible demand annually, distributed solar and fuel-based generators will add 20 GW to 35 GW a year and up to 24 GWh of capacity a year from stationary batteries. “Rather than viewing the massive adoption of EV and other DERs just as load to serve, utilities and regional grid operators can view this as an opportunity to increase the flexibility of the grid and more efficiently use existing resources and infrastructure,” DOE said. Buying peaking capacity from a VPP made of residential smart thermostats, smart water heaters, home managed EV charging, and behind-the-meter batteries can be 40% lower net cost to a utility than buying capacity from a utility-scale battery and 60% lower than from a gas peaker plant, DOE said, citing a May report by The Brattle Group.” #VirtualPowerPlants