Technology

🇨🇳🇺🇸 Chinese “𝐤𝐢𝐥𝐥 𝐬𝐰𝐢𝐭𝐜𝐡𝐞𝐬” capable of crippling power grids have been found in equipment at US solar farms, - The Times The devices, including hidden cellular radios, were discovered in Chinese inverters used to connect solar panels and wind turbines to grids worldwide. ❗️ These hidden cellular radios could be activated remotely to cripple power grids in the event of a confrontation between China and the West Engineers in American solar farms have found "𝐤𝐢𝐥𝐥 switches" in Chinese-made components, which raised severe fears that Beijing might have the power to manipulate supplies or "physically destroy" grids across the US, #UK and #Europe as per a report. Unauthorized communication devices were discovered inside some solar power inverters, reported Reuters. The devices, not mentioned in product documentation, were found by US experts who strip equipment hooked to grids to check for security issues. 🔍 Currently, energy officials are trying to find the risks posed by the small communication devices in power inverters, which are an integral part of renewable energy systems that connect them to the power grid. Though inverters are made in a way that allows remote access for updates and maintenance, the utility companies using them usually install firewalls to prevent direct communication back to China 🎤 Former director of the #USA National #Security Agency, Mike Rogers said, "We know that China believes there is value in placing at least some elements of our core infrastructure at risk of destruction or disruption," adding, "I think that the Chinese are, in part, hoping that the widespread use of inverters limits the options that the West has to deal with the security issue," quoted Daily Mail. In our endless efforts to reach #Sustainability goals by installing cheap solar panels, have we made our #Energy sectors vulnerable to outside forces who care not for #environment in the slightest? #Journalism

🎢 How To Roll Out New Features Without Breaking UX. Practical guidelines to keep in mind before releasing a new feature ↓ 🚫 We often assume that people don’t like change. 🤔 But people go through changes their entire lives. ✅ People accept novelty if they understand/value it. ✅ But: breaking changes disrupt habits and hurt efficiency. ✅ Roll out features slowly, with multiple layers of testing. ✅ First, study where a new feature fits in key user journeys. ✅ Research where different user types would find and apply it. ✅ Consider levels of proficiency: from new users to experts. ✅ Actively support existing flows, and keep them a default. 🚫 Assume low adoption rate: don’t make a feature mandatory. ✅ First, test with internal employees and company-wide users. ✅ Then, run a usability testing with real users and beta testers. ✅ Then, test with users who manually opt in and run a split-test. ✅ Allow users to try a new feature, roll back, dismiss, remind later. ✅ Release slowly and gradually and track retention as you go. As designers, we often focus on how a new feature fits in the existing UI. Yet problems typically occur not because components don’t work visually, but rather when features are understood and applied in unexpected ways. Rather than zooming in too closely, zoom out repeatedly to see a broader scope. Be strategic when rolling out new versions. Especially in complex environments, we need to be rather cautious and slow, especially when operating on a core feature. That’s a strategy you could follow in such scenarios: 1. Seek and challenge assumptions. 2. Define how you’ll measure success. 3. Have a rollback strategy in place. 4. Test with designers and developers. 5. Test with internal company-wide users. 6. Test with real users in a usability testing. 7. Start releasing slowly and gradually. 8. Test with beta testers (if applicable). 9. Test with users who manually opt in. 10. Test with a small segment of customers first. 11. Split-test the change and track impact. 12. Wait and track adoption and retention rates. 13. Roll out a feature to more user segments. 14. Run UX research to track usage patterns. 15. Slowly replace deprecated flows with the new one. With a new feature, the most dangerous thing that can happen is that loyal, experienced users suddenly lose their hard-won efficiency. It might be caused by oversimplification, or mismatch of expectations, or — more often than not — because a feature has been designed with a small subset of users in mind. As we work on a shiny new thing, we often get blinded by our assumptions and expectations. What really helps me is to always wear a critical hat in each design crit. Relentlessly question everything. Everything! One wrong assumption is a goldmine of disastrous decisions waiting to be excavated. [continues in comments ↓]

As a client project manager, I consistently found that offshore software development teams from major providers like Infosys, Accenture, IBM, and others delivered software that failed 1/3rd of our UAT tests after the provider's independent dedicated QA teams passed it. And when we got a fix back, it failed at the same rate, meaning some features cycled through Dev/QA/UAT ten times before they worked. I got to know some of the onshore technical leaders from these companies well enough for them to tell me confidentially that we were getting such poor quality because the offshore teams were full of junior developers who didn't know what they were doing and didn't use any modern software engineering practices like Test Driven Development. And their dedicated QA teams couldn't prevent these quality issues because they were full of junior testers who didn't know what they were doing, didn't automate tests and were ordered to test and pass everything quickly to avoid falling behind schedule. So, poor quality development and QA practices were built into the system development process, and independent QA teams didn't fix it. Independent dedicated QA teams are an outdated and costly approach to quality. It's like a car factory that consistently produces defect-ridden vehicles only to disassemble and fix them later. Instead of testing and fixing features at the end, we should build quality into the process from the start. Modern engineering teams do this by working in cross-functional teams. Teams that use test-driven development approaches to define testable requirements and continuously review, test, and integrate their work. This allows them to catch and address issues early, resulting in faster, more efficient, and higher-quality development. In modern engineering teams, QA specialists are quality champions. Their expertise strengthens the team’s ability to build robust systems, ensuring quality is integral to how the product is built from the outset. The old model, where testing is done after development, belongs in the past. Today, quality is everyone’s responsibility—not through role dilution but through shared accountability, collaboration, and modern engineering practices.

A conversation between a QA lead and a client related to test automation. QA Lead: Good morning! I'm excited to talk to you about an important enhancement to our testing strategy: test automation. Client: Hello! I've heard a bit about test automation, but I'm not sure how it fits into our current process. We've been doing fine with exploratory testing, haven't we? QA Lead: You're right, our exploratory testing has been effective, but there's a key area where automation can greatly help. Consider how our development team typically takes two weeks to develop a new feature, and then our testers spend a week testing it. As our software grows with more features, exploratory testing becomes a bottleneck. Client: How so? QA Lead: Well, with each new feature, our testers aren't just testing the new functionality. They also need to ensure all the previous features are still working — this is called regression testing. With exploratory testing, the time required for this grows exponentially with each new feature. Client: I see. So, testing becomes slower as our software grows? QA Lead: Exactly. For instance, by the time we reach feature number 15, testing could take much longer than it did for the first feature, because testers have to cover everything we've built so far. Client: That would slow down our entire development cycle. QA Lead: Right, and this is where test automation comes in. By automating repetitive and regression tests, we can execute them quickly and frequently. This dramatically reduces the time required for each testing cycle. Client: But does this mean we're replacing exploratory testing with automation? QA Lead: Not at all. Test automation doesn't replace exploratory testing; it complements it. There will always be a need for the human judgment and creativity that exploratory testers provide. Automation takes care of the repetitive, time-consuming tasks, allowing our exploratory testers to focus on more complex testing scenarios and exploratory testing. Client: That sounds like a balanced approach. So, we speed up testing without losing the quality that exploratory testing brings? QA Lead: Precisely. This combination ensures faster release cycles, maintains high quality, and keeps testing costs under control over the long term. It's a sustainable approach for growing software projects like ours. Client: Understood. Implementing test automation seems like a necessary step to keep up with our software development. Let's proceed with this strategy. QA Lead: Excellent! I'm confident that this will significantly improve our testing efficiency and overall product quality. #testautomation #exploratorytesting #regression #QA #testing

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗳 𝘁𝗵𝗲 𝗽𝗮𝗻𝗲𝗹𝘀 𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝗶𝘁𝘆 𝗮𝗿𝗲 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝘀𝗽𝘆𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂? A recent discovery in the US—spyware embedded in solar panels, most of them sourced from China—should be front page news everywhere. But the silence is deafening. 𝗔𝘂𝘀𝘁𝗿𝗮𝗹𝗶𝗮 𝗴𝗲𝘁𝘀 𝗼𝘃𝗲𝗿 𝟵𝟬% 𝗼𝗳 𝗶𝘁𝘀 𝘀𝗼𝗹𝗮𝗿 𝗽𝗮𝗻𝗲𝗹𝘀 𝗳𝗿𝗼𝗺 𝗖𝗵𝗶𝗻𝗮. 𝗘𝘂𝗿𝗼𝗽𝗲? 𝗡𝗼𝘁 𝗳𝗮𝗿 𝗯𝗲𝗵𝗶𝗻𝗱. 𝗧𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗶𝘀 𝗴𝗼𝗼𝗱, 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵 𝗶𝘀 𝘀𝗹𝗶𝗰𝗸, 𝗮𝗻𝗱 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝗴𝗼 𝗴𝗿𝗲𝗲𝗻, 𝗳𝗮𝘀𝘁. But what if you’re not just buying clean energy, but also an invisible backdoor? Security used to mean locking the server room. Now it means checking if your grid has been compromised before the lights even turn on. This isn’t fearmongering. It’s a reminder that “cheap and easy” can have invisible costs—especially when critical infrastructure is on the line. If you’re in renewables, procurement, or national security: Are you ready to bet your country’s grid on an untrusted supply chain? Or will you start asking the tougher questions before the breach hits home? The future is bright—but only if we remember to keep an eye on what’s powering it. https://lnkd.in/eqxPYRKR

Stuck in Space for 9 Months: What NASA Can Teach Us About Facing Challenges. Ever planned for 8 days but ended up staying 286? This week, NASA astronauts Sunita Williams and Butch Wilmore finally returned to Earth, after a jaw-dropping 286 days on the International Space Station (ISS). Originally, they were supposed to be there just 8 days! What went wrong? During their June 2024 mission on Boeing’s Starliner, the craft’s helium system faced multiple leaks. Several reaction control thrusters failed during docking. Because the return trip became too risky, NASA extended their stay to fix the problem. Sounds familiar? In business, unexpected issues and delays can happen anytime: → A last-minute code failure before a product launch → A key vendor backing out of a crucial deal → Regulatory changes that derail months of planning → A market downturn forcing a major strategy pivot What did NASA do right? They didn’t rush a risky return. They dug deeper into the problem, diagnosing and fixing root causes. Meanwhile, Williams and Wilmore contributed to scientific research and spacewalks—making the most of their extended stay. In business, a well-thought-out response is key. Identify the root cause to avoid repeating mistakes. Ask: “How do we prevent this from happening again?” Final Outcome: After nine months (instead of eight days!), the astronauts landed safely off the Florida coast. Fail-safe planning won the day. Lessons for Work: Turn setbacks into opportunities for growth and innovation—rather than panicking about “lost” time. Problem-solve for long-term improvements, not just a quick return to “business as usual.” Over to You: Have you ever faced a seemingly small hurdle that ballooned into a massive challenge? Did you treat it as a chance to learn and innovate, or did you rush to patch things up? #Leadership #ProblemSolving #BusinessLessons

AI-powered malware isn’t science fiction—it’s here, and it’s changing cybersecurity. This new breed of malware can learn and adapt to bypass traditional security measures, making it harder than ever to detect and neutralize. Here’s the reality: AI-powered malware can: 👉 Outsmart conventional antivirus software 👉 Evade detection by constantly evolving 👉 Exploit vulnerabilities before your team even knows they exist But there’s hope. 🛡️ Here’s what you need to know to combat this evolving threat: 1️⃣ Shift from Reactive to Proactive Defense → Relying solely on traditional tools? It’s time to upgrade. AI-powered malware demands AI-powered security solutions that can learn and adapt just as fast. 2️⃣ Focus on Behavioral Analysis → This malware changes its signature constantly. Instead of relying on patterns, use tools that detect abnormal behaviors to spot threats in real time. 3️⃣ Embrace Zero Trust Architecture → Assume no one is trustworthy by default. Implement strict access controls and continuous verification to minimize the chances of an attack succeeding. 4️⃣ Invest in Threat Intelligence → Keep up with the latest in cyber threats. Real-time threat intelligence will keep you ahead of evolving tactics, making it easier to respond to new threats. 5️⃣ Prepare for the Unexpected → Even with the best defenses, breaches can happen. Have a strong incident response plan in place to minimize damage and recover quickly. AI-powered malware is evolving. But with the right strategies and tools, so can your defenses. 👉 Ready to stay ahead of AI-driven threats? Let’s talk about how to future-proof your cybersecurity approach.

Transforming Healthcare Communication: Why It's Time to Say Goodbye to Fax Machines In an era where technology is at the heart of innovation, the healthcare sector stands at a crucial crossroads. While we've embraced advancements in diagnostics, treatment, and patient care, one aspect still needs to be updated: the reliance on fax machines for communication. 🔍 The Challenge: Fax machines, once revolutionary, are now a bottleneck for efficiency and a source of frustration in hospitals. They represent an outdated method that slows down processes and poses risks to data security and patient confidentiality. The Solution: Integrating Artificial Intelligence (AI) in hospital communication systems. AI offers a seamless, faster, and more reliable method of managing patient information, referrals, and critical health data. Benefits of Transitioning to AI: Enhanced Efficiency: AI can process and analyze information much faster than traditional methods, reducing wait times and improving patient care. Improved Accuracy: With AI, the risk of human error is significantly reduced, ensuring that critical patient information is always correct and current. Better Data Security: AI systems offer advanced encryption and security protocols, safeguarding sensitive patient information far more effectively than physical fax documents. Accessibility: AI-driven platforms can be accessed from anywhere, allowing healthcare professionals to share and review patient information and improving collaboration across departments and specialties. 🔗 Moving Forward, Not Backwards: It's time for the healthcare sector to embrace the digital age fully. The shift from fax machines to AI is not just an upgrade; it's a transformation that can enhance healthcare delivery, making it safer, faster, and more efficient. Let's lead the charge in making healthcare communication smarter, safer, and more suited to the needs of the 21st century. The future of healthcare depends on our ability to innovate and adapt. #HealthcareInnovation #DigitalTransformation #ArtificialIntelligence #ModernHealthcare

What if everything you know about software architecture conflicts with how the real world actually works? Listen to my conversation with Barry O'Reilly as we explore a radical rethinking of software architecture, one that embraces uncertainty and complexity. This is one of those truly intriguing conversations that reshapes my perspectives on software architecture. I was deeply immersed in the research and preparation for this episode, especially when reading Barry's thought-provoking books. Key topics we cover in this episode: ⤷ 𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁’𝘀 𝗣𝗮𝗿𝗮𝗱𝗼𝘅: Why rigid logic fails when applied to human systems and business complexity. ⤷ 𝗧𝗵𝗲 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗼𝗳 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Why requirements engineering and rigid models often fall short in practice. ⤷ 𝗥𝗲𝘀𝗶𝗱𝘂𝗮𝗹𝗶𝘁𝘆 𝗧𝗵𝗲𝗼𝗿𝘆: A revolutionary approach focused on how systems collapse and adapt over time. ⤷ 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗻𝗲𝘀𝘀 𝘃𝘀. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹𝗶𝘁𝘆: Designing architectures that survive off-spec scenarios rather than aiming for perfection. ⤷ 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆 𝗶𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Unpacking hidden “default” philosophies that shape how we build software – and why they need to change. ⤷ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 𝗳𝗼𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀: Humility, pessimism, and embracing uncertainty as tools for success.  Whether you’re a developer, architect, or business leader, this episode will challenge your assumptions and inspire new ways of thinking about software architecture!

👻 𝐄𝐯𝐞𝐫 𝐢𝐧𝐡𝐞𝐫𝐢𝐭𝐞𝐝 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐟𝐞𝐥𝐭 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞 𝐚 𝐡𝐚𝐮𝐧𝐭𝐞𝐝 𝐟𝐨𝐫𝐞𝐬𝐭 𝐭𝐡𝐚𝐧 𝐚 𝐰𝐞𝐥𝐥-𝐨𝐢𝐥𝐞𝐝 𝐦𝐚𝐜𝐡𝐢𝐧𝐞? Imagine this: You build a software system under tight deadlines, and it works 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐥𝐲. Your company saves money , and the project is hailed as a 𝐬𝐮𝐜𝐜𝐞𝐬𝐬. 𝐓𝐡𝐞𝐧, 𝐲𝐨𝐮 𝐦𝐨𝐯𝐞 𝐨𝐧. Months later, the new team tasked with maintaining your system struggles to make even minor updates. 𝐁𝐮𝐠𝐬 pile up, 𝐝𝐞𝐚𝐝𝐥𝐢𝐧𝐞𝐬 slip, and what once felt like a win now looks like a 𝐡𝐚𝐮𝐧𝐭𝐞𝐝 𝐟𝐨𝐫𝐞𝐬𝐭. 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐡𝐚𝐩𝐩𝐞𝐧? Because software isn’t just code. 𝐈𝐭’𝐬 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞. 🧠 Great software design isn’t just about delivering clean, functional code, it’s about 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐬𝐡𝐚𝐫𝐞𝐝 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐨𝐟 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦: 🎯 Why specific choices were made. 🗺️ How the system maps to real-world needs. 🤔 What changes it can handle and why. 🚧When this understanding is lost, teams face what Peter Naur calls the "death" of a program: the code runs, but no one knows how to adapt it intelligently. Fixes become guesses, and progress stalls. 𝐓𝐡𝐞 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲? Every 𝐩𝐮𝐥𝐥 𝐫𝐞𝐪𝐮𝐞𝐬𝐭, 𝐜𝐨𝐦𝐦𝐞𝐧𝐭, and 𝐦𝐞𝐞𝐭𝐢𝐧𝐠 should build 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 should make it easier for future teams to understand the system, not just how it works, but why it exists in its current form. So next time you’re coding or designing, ask yourself: 💡 “𝐖𝐢𝐥𝐥 𝐭𝐡𝐢𝐬 𝐡𝐞𝐥𝐩 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐩𝐞𝐫𝐬𝐨𝐧 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐦𝐞𝐧𝐭𝐚𝐥 𝐦𝐨𝐝𝐞𝐥 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐬𝐲𝐬𝐭𝐞𝐦?” #SoftwareEngineering #SoftwareDesign #Knowledge #Teamwork #LegacyCode