Aditya Chugh Amar
Santa Clara, California, United States
9K followers
500+ connections
9K followers
500+ connections
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Sr. Principal Engineer / Tech. Lead having hands-on expertise in software design…
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9K followers
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Aditya Chugh Amar shared thisAditya Chugh Amar shared thisTop Kubernetes Scaling Strategies You Must Know 1 - Horizontal Pod Autoscaling or HPA Horizontal Pod Autoscaler automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed CPU utilization, memory usage, or custom metrics. 2 - Vertical Pod Autoscaling or VPA Based on application requirements, VPA adjusts the resources allocated to individual pods, such as CPU and memory. This approach dynamically changes pod resource settings based on workload metrics. 3 - Cluster Auto Scaling The Cluster Autoscaler automatically adjusts the number of nodes in a Kubernetes cluster. It interacts with the cloud provider to add or remove nodes based on requirements. This is important to maintain a balanced cluster. 4 - Predictive Auto Scaling Predictive Autoscaling uses machine learning to forecast future resource requirements. It helps Kubernetes adjust resources by anticipating workload demands. Over to you: Which other Kubernetes Scaling Strategy will you add to the list? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/bbg-social #systemdesign #coding #interviewtips .
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Aditya Chugh Amar reposted thisAditya Chugh Amar reposted thisThis blog explores the emerging solutions in scale-up connectivity for rack-scale accelerator systems, focusing on UALink and Broadcom’s Scale-Up Ethernet (SUE). These interconnects are designed to address the specific demands of memory-semantic communication between accelerators, enabling efficient data transfer with minimal overhead. I attempt to analyze their protocol architectures, latency optimizations, reliability features, and performance metrics to evaluate their readiness for powering next-generation AI systems at scale. A good read for the weekend if you're interested in the future of AI infrastructure... #UALINk #Ethernet #IEEE #Broadcom #switch #JuniperNetworks #AI #Accelerators #GPU #OCP
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Aditya Chugh Amar reposted thisAditya Chugh Amar reposted thisThe Open Source AI Stack You don’t need to spend a fortune to build an AI application. The best AI developer tools are open-source, and an excellent ecosystem is evolving that can make AI accessible to everyone. The key components of this open-source AI stack are as follows: 1 - Frontend To build beautiful AI UIs, frameworks like NextJS and Streamlit are extremely useful. Also, Vercel can help with deployment. 2 - Embeddings and RAG libraries Embedding models and RAG libraries like Nomic, JinaAI, Cognito, and LLMAware help developers build accurate search and RAG features. 3 - Backend and Model Access For backend development, developers can rely on frameworks like FastAPI, Langchain, and Netflix Metaflow. Options like Ollama and Huggingface are available for model access. 4 - Data and Retrieval For data storage and retrieval, several options like Postgres, Milvus, Weaviate, PGVector, and FAISS are available. 5 - Large-Language Models Based on performance benchmarks, open-source models like Llama, Mistral, Qwen, Phi, and Gemma are great alternatives to proprietary LLMs like GPT and Claude. Over to you: Which other tool will you add to the Open Source AI Stack? – Subscribe to our newsletter to download the 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭. After signing up, find the download link on the success page: https://bit.ly/3tiMC1B #systemdesign #coding #interviewtips .
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Aditya Chugh Amar reposted thisAditya Chugh Amar reposted thisShoutout to the team that built https://lnkd.in/g3Y-Zj3W . Really neat site that benchmarks the speed of different LLM API providers to help developers pick which models to use. This nicely complements the LMSYS Chatbot Arena, Hugging Face open LLM leaderboards and Stanford's HELM that focus more on the quality of the outputs. I hope benchmarks like this encourage more providers to work on fast token generation, which is critical for agentic workflows!AI Model & API Providers Analysis | Artificial AnalysisAI Model & API Providers Analysis | Artificial Analysis
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Aditya Chugh Amar shared thisAditya Chugh Amar shared thisLinux Boot Process Illustrated The diagram below shows the steps. Step 1 - When we turn on the power, BIOS (Basic Input/Output System) or UEFI (Unified Extensible Firmware Interface) firmware is loaded from non-volatile memory, and executes POST (Power On Self Test). Step 2 - BIOS/UEFI detects the devices connected to the system, including CPU, RAM, and storage. Step 3 - Choose a booting device to boot the OS from. This can be the hard drive, the network server, or CD ROM. Step 4 - BIOS/UEFI runs the boot loader (GRUB), which provides a menu to choose the OS or the kernel functions. Step 5 - After the kernel is ready, we now switch to the user space. The kernel starts up systemd as the first user-space process, which manages the processes and services, probes all remaining hardware, mounts filesystems, and runs a desktop environment. Step 6 - systemd activates the default. target unit by default when the system boots. Other analysis units are executed as well. Step 7 - The system runs a set of startup scripts and configure the environment. Step 8 - The users are presented with a login window. The system is now ready. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq #systemdesign #coding #interviewtips .
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Aditya Chugh Amar shared thisAditya Chugh Amar shared thisNetworking is heartbeat of Kubernetes 👇 Your containers, pods and services come to life with connectivity. Today, shall we discover the happy side of understanding Kubernetes networking? 🤠 So, today let's start from the absolute basics - IPs. There are 3 kinds of IPs that you need to understand in Kubernetes. 1: Node IP ↳ The address of the physical machine or VM that hosts Kubernetes. → Pod to Pod communication across different nodes happens via node IP. 2: Service IP ↳ A virtual IP address that represents a group of Pods. → It is assigned by Kubernetes to facilitate application exposure within the cluster or externally. 3: Pod IP - ↳ The address of a single pod. → It is used by the pod to communicate with other pods and services. → The Pod IP is assigned by the Kubernetes network plugin. 2 IMPORTANT points to note: - The Node IP, Service IP, and Pod IP address do not have to be in the same network. - Kubernetes uses DNS to resolve the names of pods and services to their IP addresses. Kubernetes IP addressing may be a small thing → but they make big things happen. That's it for today. If this is useful, do a Repost. It really helps♻️ Follow me Mutha Nagavamsi for simplified Kubernetes & Technology content. You can find me on Medium and Twitter. Just search for my name on Google. Before you leave, don't forget to SMILE 😁 #kubernetes #sre #devops
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Aditya Chugh Amar reposted thisAditya Chugh Amar reposted thisOne of the key challenges in writing a textbook is to put yourself in the shoes of your reader, who doesn't necessarily share your background. I find this particularly true when writing about topics that I've been very close to, of which network virtualization is a salient example. I find there is still plenty of confusion about what it means to virtualize networks, so in this week's newsletter, I wind the clock back to the early 2000s, the first time I heard about the impact that virtual machines were having on networking. At first I was surprised that server virtualization was becoming so popular, and that surprise deepened when I started to hear some of the ideas about how to "fix" networks to support virtualization. As network virtualization emerged in the late 2000s, it became more than just a way to support the networking needs of virtual machines. Virtualization provides a near-perfect illusion of the physical system being virtualized and then changes the laws of physics to create entirely new capabilities.
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Aditya Chugh Amar reacted on thisAditya Chugh Amar reacted on thisMuch of what we aim to do at Systems Approach LLC is to make technology understandable and accessible to a wide audience. The decentralised architecture of the Internet and the fact that most functionality is implemented at the edges has been a force for technological "democratisation", in the sense that almost anyone could create new applications, publish content, and so on. This "open to all" approach is less prevalent in the mobile wireless world, which is why lately we've been working on bringing 5G technologies to a wider audience through our Private 5G book and open source software efforts. Our latest newsletter has the details. #5G
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Aditya Chugh Amar shared thisAditya Chugh Amar shared thisMuch of what we aim to do at Systems Approach LLC is to make technology understandable and accessible to a wide audience. The decentralised architecture of the Internet and the fact that most functionality is implemented at the edges has been a force for technological "democratisation", in the sense that almost anyone could create new applications, publish content, and so on. This "open to all" approach is less prevalent in the mobile wireless world, which is why lately we've been working on bringing 5G technologies to a wider audience through our Private 5G book and open source software efforts. Our latest newsletter has the details. #5G
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2y
Kudos Puneet Jindal . Very well deserved !!
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisToday we’re introducing the Poolside Platform: a production-grade system for running AI agents inside your boundary. For the past few years, many organizations have had to make uncomfortable tradeoffs to use AI: send sensitive data outside their environment, accept pricing they can’t fully control, or relax security patterns that took years to build. We don’t think that should be the default. The Poolside Platform is built for teams that need frontier AI capability without giving up control. You choose the model and deploy it where it needs to run: on bare metal, on turnkey air-gapped hardware through partners like @delltechnologies, or inside your existing VPC on AWS, Azure, or Google Cloud. As AI usage shifts further toward metered pricing, the Platform gives businesses more control over the economics of agentic AI, with the flexibility to choose the right model, infrastructure, and deployment pattern for each workload. Once deployed, agents work where developers already spend their time, from VS Code, Visual Studio, Zed, and IntelliJ to the terminal. They connect to the systems your teams rely on, including Slack, Jira, GitHub, Workday, and Salesforce, through centrally managed MCP servers. Each session runs in a containerized environment with built-in secret management and network policy controls. Every agent action is captured as a searchable trajectory, including tool calls, file edits, decisions, and reasoning steps, so administrators can define what agents can access, control what they can do, and audit what happened after the fact. And because production looks different in every enterprise, we don’t just hand over software and leave. Our Forward Deployed Research Engineers embed with your team to learn your environment, build on the Platform, and ship the first automated workflow in weeks. Risk controls and auditability are designed with you from the start, not added later. For teams whose data is too sensitive, too regulated, or too strategic to leave their security boundary, the Poolside Platform makes AI agents deployable on your terms. Link to the full blog post in the comments.
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Aditya Chugh Amar liked thisAditya Chugh Amar liked this🚨 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿𝘀 𝗔𝗿𝗲 𝗦𝗽𝗹𝗶𝘁𝘁𝗶𝗻𝗴 𝗜𝗻𝘁𝗼 𝗧𝗪𝗢 𝗖𝗹𝗲𝗮𝗿 𝗣𝗮𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗡𝗩𝗜𝗗𝗜𝗔 𝗷𝘂𝘀𝘁 𝗺𝗮𝗱𝗲 𝗶𝘁 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 🚨 Most people think “AI skills” = prompting a chatbot. That mindset will cap your career. This NVIDIA Certification Framework shows something much more important 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘄 𝗮 𝗳𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲, and you must choose where you play. 𝗧𝗿𝗮𝗰𝗸 𝟭: 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲) For those who make AI run at scale: - AI Infrastructure - AI Operations - AI Networking Think: GPUs, clusters, performance, reliability, security, cost control. This is where 𝗗𝗲𝘃𝗢𝗽𝘀, 𝗖𝗹𝗼𝘂𝗱, 𝗦𝗥𝗘, 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 evolve next. 𝗧𝗿𝗮𝗰𝗸 𝟮: 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 & 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 (𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀 𝗼𝗳 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲) For those who make AI useful: - GenAI LLMs - Agentic AI - Accelerated Data Science - OpenUSD Development Think: RAG, agents, workflows, multimodal systems, production AI apps. 𝗔𝘀𝘀𝗼𝗰𝗶𝗮𝘁𝗲 → 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 = 𝗖𝗮𝗿𝗲𝗲𝗿 𝗟𝗮𝗱𝗱𝗲𝗿 This isn’t “learn a tool and hope”. It’s a 𝗰𝗹𝗲𝗮𝗿 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗺𝗮𝘀𝘁𝗲𝗿𝘆. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: If you don’t intentionally pick a lane (or combine both), you’ll stay stuck at 𝗱𝗲𝗺𝗼-𝗹𝗲𝘃𝗲𝗹 𝗔𝗜 while others build careers. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂: Are you more excited by running AI at scale or building intelligent systems? ♻️ Repost if this helped you see AI careers more clearly 🔔 Follow for more AI, Cloud & career insights #AI #ArtificialIntelligence #GenAI #AgenticAI #MachineLearning #CloudComputing #DevOps #MLOps #PlatformEngineering #DataScience #AICareers #TechCareers #Upskilling #NVIDIA #Certifications
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisLearn how 500k+ hours of Mandiant investigations are reshaping cyber strategy. Align your security posture with evidence-based business risks.
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisIf you're serious about AI engineering (in 2026), then learn these 13 concepts: 1 How Vector Database Works → https://lnkd.in/dbeBn5Un 2 How RAG Works → https://lnkd.in/dbAUacYW 3 Design Personal Chat Assistant → https://lnkd.in/d9KG99zV 4 LLM Concepts - A Deep Dive → https://lnkd.in/eSd6fS7n 5 How to Design an AI Agent → https://lnkd.in/dGUknFw3 6 What is Reinforcement Learning → https://lnkd.in/dzSXrgNW 7 AI concepts 101 → https://lnkd.in/en7Vz3E7 8 LLM Evals 101 → https://lnkd.in/diU4hic8 9 Context Engineering 101 → https://lnkd.in/d4WNwfqY 10 AI Coding Workflow 101 → https://lnkd.in/ds5r8TxT 11 Agentic Patterns, Simply Explained → https://lnkd.in/dfsAsc7c 11 How AI Agents Work → https://lnkd.in/dU8CK7-b 12 Multi-Agent Architectures, Clearly Explained → https://lnkd.in/dAM4u9Si 13 How MCP Works → https://lnkd.in/eT-z8Ekk What else should make this list? === 👋 PS - Want my System Design Playbook for FREE? Join my newsletter with 200K+ software engineers now: → https://lnkd.in/ehTrcyak === 💾 Save now & repost to help others learn AI engineering. 👤 Follow Neo Kim + turn on notifications.
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisWe are proud to feature Aman Pal Singh , Founder & Managing Director of Benefits for Expats Inc. Redefining insurance through technology and inclusion. A Leader to Watch in 2026. His leadership stands at the intersection of AI governance, cross-border strategy, and digital transformation reshaping how insurers across the MENA region build scalable, accountable growth models. As innovation accelerates, one insight remains clear: Growth without governance is acceleration without direction. 🔗 Read the full cover story: https://lnkd.in/dF6awG6t
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisI’m honored to be serving as the 2026 Light The Night Corporate Walk Chair, leading our Executive Leadership Committee here in Silicon Valley with Blood Cancer United On Wednesday, May 13, 2026, our Executive Committee is hosting the Light The Night Leadership Launch at the Mayfair Community Center, and I’d love to personally invite fellow community and business leaders to join us. This event is an opportunity to: ✨ Learn how Light The Night supports families facing blood cancer ✨ Connect with purpose‑driven leaders across Silicon Valley ✨ Explore how your organization can engage employees while making a meaningful impact 📍 Location: Mayfair Community Center 🕠 Registration: 5:30 PM 🕕 Program: 6:00–7:00 PM (with networking before and after) If you’re interested in attending or learning more, please RSVP using the link below or contact Carina Coppedge at 253‑590‑8770 or Carina.Coppedge@BloodCancerUnited.org. I hope you’ll consider being my guest — and joining us in bringing light to the darkness of cancer. 👉 RSVP here: https://lnkd.in/e9AueFFf #LightTheNight #LeadershipForACause #CorporateLeadership #SiliconValley #BloodCancerAwareness #PurposeDriven
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisMost people think they understand AI. Until they realise they only recognise buzzwords. I’ve curated the 40 important AI terms here to fix that gap. A few months ago, I noticed something strange. Smart professionals were using AI every day. But struggling to explain it clearly. They could say "LLM", "agents", or “hallucinations”. But not understanding what those actually mean or why they matter. That is the difference between using AI. And thinking with AI. So I broke it down here. Why this matters more than people realise: → Clarity compounds faster than just using AI tools → Understanding beats memorising prompts → Vocabulary shapes how well you use AI A few terms most people misuse daily: • Bias → When training data tilts outputs • Tokens → How models read and write text • Inference → Using learned knowledge in real time • Overfitting → When AI memorizes instead of generalizing • Hallucinations → Confident answers without factual grounding Do’s: ✅ Learn concepts before tools ✅ Connect terms to real workflows ✅ Understand limits, not just capabilities ✅ Revisit fundamentals regularly ✅ Explain ideas in your own words Don’ts: ❎ Chasing tools without understanding basics ❎ Confusing AGI with current LLMs ❎ Ignoring training and data quality ❎ Treating prompts as everything ❎ Assuming AI is always correct Learn AI for free: https://lnkd.in/euYZeAdb If you want leverage from AI, You must speak its language first. To get a quick scan of all 40 AI terms, Check the infographic below 👇 How much time do you invest in learning AI every day? Comment below 👇
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisBeen thinking about this one for a while. #SONiC is hitting $5B by 2027 according to 650 Group's Alan Weckel, the enterprise adoption curve is finally starting to reflect what a lot of us knew was coming. I wrote a bit about the full arc: Phase I of open networking at Dell, what it actually took to productize Enterprise SONiC, and why the pitch has completely shifted from cost reduction to AI infrastructure readiness. Worth a read if you are building or selling into the data center networking space right now. #OpenNetworking #SONiC #AIInfrastructure #AINetworking #DataCenterNetworking #CloudNetworking #EnterpriseIT #NetworkStrategy #GTMSONiC Is Going Mainstream, and the Timing Could Not Be BetterSONiC Is Going Mainstream, and the Timing Could Not Be Better
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Aditya Chugh Amar liked thisAditya Chugh Amar liked thisMost enterprise networks were designed to connect systems first and secure them later. Breaches consistently follow a pattern of initial compromise followed by lateral movement within the network. This isn't random; it's a reflection of how enterprise networks were originally designed. In an earlier post, I shared some of the data behind this pattern for campus and branch networks. For decades, campus network design prioritized availability and reachability, aiming simply to connect users, systems, and buildings reliably. While security controls existed, they were primarily focused on the perimeter. Connectivity was managed through VLANs, subnets, and routing, which often broadly permitted "east-west" communication inside shared domains. Network Access Control verified identity upon joining, but enforcement typically ended there. This model made sense when most devices were corporate-managed and the main threat was external intrusion. Today, the threat model has shifted. Attackers assume they will gain initial access and then move laterally. Zero Trust adopts a different starting assumption: breach is inevitable. This assumption fundamentally conflicts with architectures built to communicate first and secure later. Before adding more complexity—overlays, appliances, or policy engines—a more basic question must be asked: Can an architecture built on implicit internal trust reliably enforce Zero Trust? This is the core structural challenge. Nile #zerotrust #NaaS
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