Understanding Advanced Computing

Every Senior DevOps should save this now. Because 2026 is coming through default changes, deprecations, runtime shifts, and ecosystem upgrades that will slowly make your “stable” production behave differently. Here’s what 2026 actually means. Ingress-NGINX is retiring (March 2026). If you’re still running it, you’re on borrowed time. Plan your migration to Gateway API (or another supported controller) while things are calm and not during a Sev-1. Gateway API is no longer “future talk.” It’s moving fast. Your Ingress knowledge is slowly becoming legacy, especially when teams demand richer traffic control. cgroup v2 is the real-world default now. CPU throttling patterns shift. Memory reclaim behaves differently. Old tuning assumptions don’t fully apply anymore. And subtle breakages are the new outages. Runtime upgrades are behavior changes. New containerd versions, new node images, new defaults. Re-test probes, shutdown paths, limits, PDBs. The “boring” paths are where 2026 incidents will hide. OpenTelemetry is spreading everywhere. Not just traces, pipelines too. CI/CD health becomes first-class telemetry, not just “green build = fine.” Kubernetes is now the default AI platform. Which means GPUs, quotas, and cost controls are your problem. Expect driver drift, GPU fragmentation, and runaway autoscaling. FinOps is runtime, not quarterly. Teams lose money from defaults: No limits. Over-requests. Zombie volumes. Egress leaks. Logging explosions. Guardrails must live in policies, not slides. The real 2026 “Senior DevOps” skill? Migration readiness. Ingress → Gateway. Agents → OTel. cgroup v1 thinking → cgroup v2 reality. Classic apps → AI workloads. Your job isn’t firefighting. It’s making change boring.

As cloud-native technologies mature, the landscape is rapidly evolving. Let's explore the emerging trends and advanced concepts that are shaping the future of cloud computing. Next-Gen Cloud-Native Concepts: 1. 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴    - Bringing computation closer to data sources    - Reduces latency, enhances real-time processing    - Key for IoT, AR/VR, and 5G applications 2. 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀    - Combines benefits of serverless and containerization    - Examples: AWS Fargate, Azure Container Instances    - Simplifies operations while maintaining container flexibility 3. 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗠𝗲𝘀𝗵    - Advanced network communication layer for microservices    - Improves security, observability, and traffic management    - Popular tools: Istio, Linkerd, Consul 4. 𝗚𝗶𝘁𝗢𝗽𝘀    - Infrastructure-as-Code taken to the next level    - Uses Git as a single source of truth for declarative infrastructure    - Enhances collaboration, versioning, and auditing 5. 𝗙𝗶𝗻𝗢𝗽𝘀    - Brings financial accountability to cloud spend    - Optimizes resources across business, finance, and tech teams    - Critical for managing costs in complex cloud environments 6. 𝗔𝗜𝗢𝗽𝘀    - Applies AI to IT operations    - Enhances anomaly detection, predictive maintenance    - Automates routine tasks, improves system reliability 7. 𝗖𝗵𝗮𝗼𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴    - Intentionally injecting failures to improve resilience    - Identifies weaknesses in distributed systems    - Tools like Chaos Monkey help build more robust applications 8. 𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (𝗗𝗲𝘃𝗦𝗲𝗰𝗢𝗽𝘀)    - Shift-left approach to security in CI/CD pipelines    - Emphasizes automated security testing and compliance checks    - Examples: Snyk, Aqua Security, Twistlock Why These Matter: - Push the boundaries of performance and efficiency - Address emerging challenges in distributed systems - Enhance automation and reduce operational overhead - Prepare for next-generation applications and use cases Staying ahead in cloud-native development means not just mastering current technologies, but also anticipating and adapting to these emerging trends. Which of these concepts excites you the most? How do you see them impacting your current or future projects?

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

“𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝗮𝗽𝗽𝘀… 𝗮𝗻𝗱 𝗶𝘁’𝘀 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗲𝗹𝘆 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝘀.” This MIT lecture quietly does something most AI content never does. It forces you to stop thinking about tools for a minute and ask a much harder question: what is computation, really? It starts like a normal lecture. Then, before you know it, it is dismantling the way we talk about intelligence, learning, abstraction, and even what we think machines are doing when they “think.” 🎩 And just when you think MIT cannot get any more MIT… the professor puts on a wizard hat and turns eval and apply into something that feels half computer science, half spell-casting. Strange. Brilliant. Oddly unforgettable. 💡 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Because too many people are building AI careers on surface-level fluency. They know the tools. They know the demos. They know the buzzwords. But the foundations? That is often where the silence begins. And that is risky. We keep using labels that sound far more advanced than they really are: → Artificial intelligence is not truly intelligent → AI agents do not really have agency → Machines do not “learn” the way people imagine they do That is why lectures like this matter so much. They take you beneath the hype and back to the layer that actually lasts: → abstraction → evaluation → computation To me, that is the real divide in AI now. Some people are learning how to use the latest tools. Others are learning how to understand what those tools are really doing. The second group will build the future. The first group will keep reposting it. What do you think matters more in AI right now: mastering the tools, or understanding the foundations underneath them? #AI #ArtificialIntelligence #ComputerScience #MIT #MachineLearning #Innovation #Technology #FutureOfWork #Learning

Here's an uncomfortable truth: Most RTOS implementations in embedded systems are nothing but unnecessary overhead that makes developers feel sophisticated while actually introducing more problems than they solve. We've all been there, facing a complex embedded project and immediately reaching for that "industry-standard" RTOS solution. But let's be honest: in 80% of applications, an RTOS is like using a sledgehammer to crack a nut. The context switching overhead alone can consume precious CPU cycles that your resource-constrained device can't afford to spare. And don't get me started on priority inversion issues that turn your "deterministic" system into a timing lottery. I've spent countless hours debugging mysterious timing issues only to discover they were caused by the RTOS itself, nondeterministic interrupt latency, hidden system calls, and the inevitable memory fragmentation when dynamic task creation is involved. What we really need is a return to bare-metal programming with well-structured state machines and interrupt-driven design. It's not "old school", it's engineering discipline. When your microcontroller has 32KB of flash and 4KB of RAM, every byte counts, and every unnecessary abstraction is a liability. The next time you're tempted to drop an RTOS into your project, ask yourself: Do I really need it, or am I just avoiding the hard work of designing a proper architecture? 🔥 What's the most ridiculous RTOS-induced nightmare you've ever debugged? Share your war stories below, bonus points for priority inversion horror stories! #EmbeddedSystems #RTOS #BareMetal #Firmware #EmbeddedC #RealTimeSystems #LowLevelProgramming #TechTruth #EmbeddedEngineering

🇳🇱🇪🇺🇳🇱🇪🇺 Every Dutch and European thinking about AI should read Anton Leicht's new post (link in comments). His point: we always assume access to the latest AI models will be broadly available forever. That everyone in the world will get access to roughly the same best models, and that the main question is who uses them best. In Europe, you often hear this in some version of: "even if Europe loses the race to build frontier AI, we can still win economically by adopting it fastest". But this access is far from obvious or guaranteed. Anton explains why access to the most capable AI systems may become scarcer and more selective: security risks (eg with Claude Mythos), distillation concerns, compute shortages, and geopolitics could all change who gets access. Initial access might become exclusive US national security agencies, US companies, and a small circle of trusted partners. Everyone else may only get access later, through more limited models, with fewer tokens and less control. The solution is not to cross our fingers and hope we will keep access but to create leverage now so Europe has something to offer in return. Build large-scale datacenters. Build chip fabs. Secure more energy. Make it attractive (eg through tax incentives) for hyperscalers, chip makers, and frontier labs to build capacity in Europe, but tie those incentives to access guarantees. Creating leverage is also part of the reason why, in the Dutch National AI Deltaplan Jelle Prins Stan van Baarsen Renée Frissen Onno Eric Blom Oscar Lepoeter and I put so much emphasis on compute, energy infrastructure, and AI compute zones. Not because we will suddenly build the next OpenAI in the Netherlands. But because without owning the infrastructure and parts of the supply chain, we have very little leverage. And if you now think “isn’t access to second-tier models good enough?”. Maybe, for some applications. But most value ultimately comes from innovation and the ability to innovate in any domain will depend on having access to the best models. When frontier AI becomes the foundation of science, economic progress, defense, and government, access to frontier models is critical.

You're in an AI Engineer interview at Google DeepMind and the interviewer asks: "Your 1B parameter proxy model trains perfectly with a 1.2e-4 learning rate. You scale the model to 70B, and the training immediately explodes. What's the most 𝘭𝘪𝘬𝘦𝘭𝘺 reason and how do you fix it 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 running a new, expensive hyperparameter sweep?" Most candidates say: "The model is too big, so the updates are unstable. I'd add gradient clipping and just keep lowering the learning rate manually until it's stable." Wrong. That's a patch, not a solution. You're just masking the root cause and wasting millions in compute cycles trying to find a new LR. The reality: This isn't a 𝘵𝘶𝘯𝘪𝘯𝘨 problem, it's a 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 problem. You're seeing a classic failure of 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐒𝐏). In SP models, the optimal learning rate 𝘴𝘩𝘪𝘧𝘵𝘴 as you scale the model's width. The LR that was perfect for your 1B proxy is now catastrophically large for the 70B model because the update dynamics didn't scale uniformly with the parameters. The fix is to use 𝐌𝐚𝐱𝐢𝐦𝐮𝐦 𝐔𝐩𝐝𝐚𝐭𝐞 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐌𝐔𝐏). MUP is 𝘯𝘰𝘵 just another initialization scheme. It's a set of rules that scales both the initializations AND the 𝘱𝘦𝘳-𝘭𝘢𝘺𝘦𝘳 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘳𝘢𝘵𝘦𝘴 (e.g., scaling them by 1/width ). This re-parameterization does one magical thing: it makes the optimal hyperparameters, especially the learning rate, scale-invariant. This means the optimal LR you found on your cheap 1B proxy directly transfers to your 70B monster. No new sweep needed. 𝐓𝐡𝐞 𝐀𝐧𝐬𝐰𝐞𝐫 𝐓𝐡𝐚𝐭 𝐆𝐞𝐭𝐬 𝐘𝐨𝐮 𝐇𝐢𝐫𝐞𝐝 "With Standard Parameterization, you're forced to find a new, unstable learning rate for every scale. With MUP, you find the optimal LR once on a small proxy, and it remains optimal at any scale. You don't scale the LR; you build the model to fit the LR." #AI #MachineLearning #DeepLearning #LLM #ScalingLaws #MLEngineering #MUP

🧠 Today we introduce Un-0 from Unconventional AI: the first large-scale generative model build on physics as a compute primitive. This represents a “hello world” moment for physics-based models. We use the inherent time-varying behavior of physical systems to do compute for us. The result is a new way to build a computer that can be VASTLY more power efficient. Why is this significant? It shows that computing is not some unique invention from humans; it’s present all through nature and physics. All physics of all physical entities has time; however, current computing systems don’t. We’re exploiting that time dimension. How does this relate to energy efficiency? Most energy in existing von Neumann machines goes into moving information between memory and compute elements. Dynamical systems combine compute and memory into a single entity. What’s more, dynamical systems can tolerate noise. This opens up new opportunities to save energy in communication even further. Un-0 represents a big first step in changing the paradigm of compute to dynamical systems. We are connecting intelligence to dynamics with this model release. Dynamics are a natural framing for compute for AI; neural networks themselves are really dynamical systems so the mapping becomes more straightforward. The brain does not have an abstraction of linear algebra; so in effect, we’re cutting out the middleman. Churchill quote that works pretty well here (h/t CFO Ali Esfahani) ... "Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning." We are embarking on a fantastic journey 🚀 https://t.co/zYU0ezXJUq

🔐 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗦𝗵𝗼𝘂𝗹𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗢𝗦𝗜 𝗠𝗼𝗱𝗲𝗹 🌐 The OSI Model (Open Systems Interconnection) isn’t just a networking theory — it's a universal language for how data travels across systems. Whether you’re in cybersecurity, DevOps, networking, or software, mastering the OSI model gives you deep insights into how everything connects. 🧱 𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝟳 𝗟𝗮𝘆𝗲𝗿𝘀: 1️⃣ 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗟𝗮𝘆𝗲𝗿 Deals with the actual hardware — cables, switches, signals. Think: "How are bits transmitted over a wire?" 2️⃣ 𝗗𝗮𝘁𝗮 𝗟𝗶𝗻𝗸 𝗟𝗮𝘆𝗲𝗿 Responsible for node-to-node data transfer. Handles MAC addresses and error detection (like CRC). 3️⃣ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗟𝗮𝘆𝗲𝗿 Routes data from source to destination using IP addresses. Routers operate here. 4️⃣ 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 𝗟𝗮𝘆𝗲𝗿 Ensures reliable data transfer via TCP/UDP. Handles packet sequencing and acknowledgments. 5️⃣ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 Manages and controls the dialogue (sessions) between computers. Handles setup, maintenance, and termination. 6️⃣ 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 Formats or translates data for the application. Handles encryption, compression, and encoding. 7️⃣ 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 Where users interact with apps like HTTP, FTP, DNS, SMTP. The only layer visible to end-users. 🔄 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁𝗶𝗻𝗴?  𝗜𝗳 𝗮 𝘂𝘀𝗲𝗿 𝗰𝗮𝗻’𝘁 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: Is it the cable? (Layer 1) MAC address filtering? (Layer 2) IP misrouting? (Layer 3) Port blocked? (Layer 4) App crash? (Layer 7) 🎯 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The OSI model simplifies complex network issues by providing a clear, layered structure for analysis. It’s also foundational for understanding firewalls, proxies, VPNs, SIEM tools, and more. ✅ Learn it. Use it. Teach it. #Networking #OSIModel #CyberSecurity #TechTraining #Infosec #TCPIP #NetworkEngineer #CyberAwareness #Troubleshooting #DevOps #SIEM For More Updates, Follow: Kaaviya Balaji