Edge Computing Applications

🚀 Europe’s Armed Forces Face a 15km 'Death Zone'—Startups Could Be the Key to Surviving It Europe’s militaries are confronting a new battlefield reality: a 15km "zone of total death" identified from the Ukrainian frontlines, where traditional logistics and manned operations have become lethal due to drones, electronic warfare, and precision strikes. At the recent UK-Ukraine Defence Tech Forum, General Valerii Zaluzhnyi put it bluntly: “Classical offensive operations are not just ineffective—they’re suicidal in these zones.” 👉 This challenge demands a radical rethink of logistics at the tactical edge. Troops cannot risk driving trucks into these zones. Instead, quiet, electric Unmanned Ground Vehicles (UGVs) must be deployed to ferry ammunition, supplies, and even evacuate the wounded—taking humans out of harm’s way. But here’s the breakthrough: AI-driven autonomy is making this possible. Startups like TENCORE are scaling rapidly to meet this need, delivering modular UGVs capable of: ✅ Autonomous navigation in GPS- and comms-denied environments using AI-powered perception and route planning ✅ Real-time adaptation to battlefield threats without direct operator control ✅ Modular mission-switching—from logistics to mine-laying to fire support—on a single platform These vehicles are engineered for extreme resilience and flexibility: battery swaps in under 10 seconds, lego-like repairability, and minimal human intervention. But let’s be clear: 👉 Hardware is now table stakes. It’s software that will win the wars of the future. The edge lies in the software layer: AI that can navigate and decide under electronic warfare and jamming Swarming algorithms that enable distributed, coordinated missions Autonomous decision-making at the tactical edge without waiting for command uplinks 🔥 The startup opportunity? Europe’s militaries urgently need: AI-first, software-defined autonomy platforms Interoperable software ecosystems across NATO forces Rapid software iteration matching the speed of battlefield adaptation In today’s wars, humans are the most expensive and vulnerable resource. AI-enabled autonomy isn’t just a buzzword—it’s the frontline’s survival mechanism. The future of defence will be fought in code, deployed on autonomous machines. 💬 If you’re building robotics, AI, autonomy platforms, or distributed software systems, this is your moment. Let’s connect: Europe’s defence ecosystem is ready for bold innovators. #DefenceInnovation #MilitaryLogistics #UGVs #AI #AutonomousSystems #SoftwareDefinedWarfare #StartupOpportunity #EuropeanSecurity #TechForDefence #Ukraine #KARISTA #PSION #NationalSecurity #Geopolitics #DualUseTech #OmniUse #DefenceTech #VentureCapital #Investing #TechCommandInvesting

Back in 2019, before I became a Sr. Engineer, I did a mock system design with a Google EM who gave me very brutal feedback: “Nice design, but I have no idea how you will keep it healthy after launch.” At the Senior level and beyond, diagrams are expected. But how clearly you talk about observability, alerts, and recovery also matters a lot.  Here are 10 simple rules for observability and health checks I keep in my head every time I think about it, whether I am in a system design interview or doing my day-to-day work. 1. Start from SLOs – Decide what “healthy” means in numbers. – Example: 99.9% of requests under 300 ms, error rate under 0.1%, uptime 99.9% per month. 2. Use the three pillars with clear roles - Metrics for fast detection (latency, error rate, QPS, CPU, memory). - Logs for detailed context and errors. - Traces to see how a request flows across services. 3. Separate liveness and readiness checks - Liveness: is the process running. If false, kill and restart the pod. - Readiness: can this instance serve traffic. If false, load balancer stops sending requests. 4. Add layered health checks - Level 1: /healthz returns 200 if process is ok. - Level 2: /ready quickly tests DB, cache, queue. - Level 3: synthetic “user journey” checks (login, simple read) on a schedule. 5. Track golden signals for every important API For each service, expose:    - Latency (p50, p95, p99)    - Traffic (QPS / RPS)    - Errors (5xx, 4xx)    - Saturation (CPU, memory, queue length). 6. Use correlation IDs end to end Generate a request ID at the edge. Pass it through all services and logs. This lets you trace a single user request across the system during debugging. 7. Design dashboards for oncall use  One main dashboard per service.  Top section: SLOs and golden signals.  Next section: dependency health (DB, cache, queues).  Keep charts focused and readable. 8. Create meaningful alerts Alert on symptoms that hurt users.  Each alert should map to a clear runbook step. Example: - p95 latency above threshold for 5 minutes - error rate above threshold - no traffic when you expect traffic. 9. Use feature flags and slow rollouts – Roll out new features to a small percent of traffic. – Watch metrics and logs for regressions. – Increase traffic only when the system stays healthy. – Roll back quickly if SLOs drop. 10. Practice incident response and postmortems – Keep runbooks for common failures: “DB down”, “cache unhealthy”, “one region flaky”. – After incidents, write short postmortems, update dashboards, alerts, and code. This is how observability keeps improving over time. – P.S: I've just created an account on Twitter, follow me for more such insights there: https://lnkd.in/g9H82Q98

A company I know deployed an AI agent in 3 days. No boundaries defined. No guardrails. No sandbox testing. No failure playbook. Week 1: It sent 400 unapproved emails to clients. This is not a horror story. This is what happens when excitement outpaces engineering. The companies succeeding with AI agents in 2026 all follow the same principle: Scaling follows confidence, not excitement. They start small. They define limits. They test adversarial scenarios. They build human approval gates. They observe before they expand. Here’s the step-by-step deployment path serious teams follow - Start with a safe, low-risk use case - Define the agent’s boundaries clearly - Map structured workflows (no guessing) - Ground it with trusted data sources - Apply least-privilege access - Add guardrails before autonomy - Choose the right architecture - Test in simulation (normal + edge cases) - Deploy in a sandbox first - Introduce human approval gates - Add observability and monitoring - Roll out gradually - Create a failure playbook - Build continuous learning loops - Implement governance & compliance controls Safe AI isn’t about slowing down innovation. It’s about engineering trust. Constrain → Ground → Test → Observe → Expand. 15-step framework. Swipe through. Your team needs this before the next sprint planning meeting. What’s the biggest mistake you’ve seen in AI agent deployment? Drop it below 👇

A year ago, I wrote about Google's Media CDN offering and its positioning in the market, which was primarily centered on leveraging Google’s network for large-scale video delivery. As with any service, the initial value proposition is only part of the story. The more telling measure is its subsequent evolution in response to customer usage and industry demands. A year later, Google has made key enhancements to its Media CDN, focusing on adding capacity and operational tooling, as well as onboarding large media and entertainment customers. The fundamental challenge for CDNs remains handling massive, concurrent traffic spikes associated with live streaming. Events over the past year, such as the Super Bowl, FIFA World Cup, and IPL, have continued to set new streaming benchmarks. 𝗢𝗻𝗲 𝗻𝗼𝘁𝗮𝗯𝗹𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗠𝗲𝗱𝗶𝗮 𝗖𝗗𝗡 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗮𝘁 𝘀𝗶𝗻𝗰𝗲 𝗲𝗮𝗿𝗹𝘆 𝟮𝟬𝟮𝟱, 𝗶𝘁 𝗵𝗮𝘀 𝘁𝗿𝗶𝗽𝗹𝗲𝗱 𝗶𝘁𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗠𝗲𝗱𝗶𝗮 𝗖𝗗𝗡 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆. Beyond raw capacity, several architectural and commercial updates have been introduced to address common customer pain points around origin performance and budget predictability. Google has added new caching and routing options, including Flexible Shielding, with shield regions in South Africa, the Middle East, and the U.S. The goal is to improve cache offload rates by keeping traffic within a region, thereby avoiding the latency and data-transit costs associated with the "hairpinning" effect of fetching content from a distant origin. It’s worth noting that this is implemented as an add-on feature, allowing customers to choose between optimizing for performance or offloading, in addition to the platform's existing multi-region caching and shielding architecture, which is offered at no cost. Full blog post: https://lnkd.in/eA_giTWw #streamingmedia #googlemediacdn #contentdelivery #infrastructure

Edge is not a trend; it’s an architecture shift. From $10B in 2023 to $50B+ by 2033... ...the growth isn’t driven by hype. It’s driven by physics. Because once you move from 100 ms to 20 ms, apps feel usable. But to cross 5 ms? You need to compute at the baseband, not the core. Here’s how to engineer edge sites that deliver deterministic low latency.. ...the kind autonomous vehicles, high-frame-rate AR, and critical IoT actually depend on: 1️⃣ Deploy true micro-edge, not retrofitted closets. Use prefabricated, hardened SmartMod™ units from Schneider Electric. Each is factory-integrated for power, cooling, fire, and control. Drop next to STC, Du, or Airtel 5G towers. Size them in 50 kW increments, enough for MEC, AI inference, or on-prem cloud functions. 2️⃣ Terminate fibre and power before you lift a panel. Edge buildouts fail when backhaul and power provisioning lag site readiness. Lock dual feeds (utility + genset), reserve dark fibre with SLA-bound loop latency. Tie telemetry into a regional NOC using EcoStruxure™ IT Expert. 3️⃣ Architect for adversarial environments. At edge, risk profiles flip. You’re no longer behind seven enterprise firewalls. Implement zero-trust gateways at entry points. Segment IoT ingress from control networks. Deploy biometric access control per rack, not just facility. 4️⃣ Design for thermal density and burst load. Run average loads at 65–70% to preserve thermal headroom. Plan cooling for non-linear spikes from MEC caching or edge GPU workloads. Active airflow control, rear-door heat exchangers, or liquid-ready chassis, depending on density. 5️⃣ Treat orchestration as a control system, not a dashboard. With EcoStruxure™, power, cooling, access, and IT converge into a decisioning plane. Don’t just monitor, let the system act. Use real-time data to preempt failure, not just alarm on it. This isn’t edge as a PoC. This is production-grade, SLA-bound, carrier-integrated infrastructure. 5G gives you bandwidth. Edge gives you responsiveness. Without both, your low-latency promise doesn’t land. Ready to design for 5 ms? Let’s draw your first edge map.

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation

Mile High Video Spotlight: Adeia’s Low-Latency Streaming Innovations At Mile High Video 2025, VP of Advanced R&D Chris Phillips detailed Adeia's approach to low-latency streaming, showcasing three key technologies: • Low Latency Streaming: Adeia minimizes delay by optimizing video segment prediction and buffering. This ensures consistent playback quality even under fluctuating network conditions, delivering a seamless viewing experience. • Encoding Optimization: Adeia uses machine learning to dynamically adjust encoding parameters based on real-time network feedback. This balances video quality and bandwidth efficiency, reducing buffering without compromising visual fidelity. • Selective L4S Markings: Adeia leverages Low Latency, Low Loss, Scalable Throughput (L4S) technology by selectively marking packets to prioritize latency-sensitive video data. This reduces delay and packet loss, enhancing reliability over congested networks. Adeia also presented a paper, “On Ultra-Low Latency Multimedia Delivery: An Approach for Selective L4S Enablement,” exploring how selective L4S marking can enhance low-latency streaming, paving the way for next-generation video delivery solutions. Chris shared his bullish outlook on VVC (Versatile Video Coding), emphasizing its potential for improved compression efficiency and enhanced video quality. For a deeper dive into Adeia’s low-latency streaming technologies, read the full interview or watch the video, both at the link below.

Most developers still use AJAX, WebSockets, or, even worse, HTTP polling to get real-time server events. And they still call it the "modern web." But they’ve never heard of Server-Sent Events (SSE), the protocol built for this use case. It’s built into every browser: - No dependencies. - No client polling. - No connection overhead. Why should you care? - Native HTTP/1.1 & HTTP/2 support. No extra handshake, no CORS headaches. - Automatic reconnection, built-in. - Streams events from the server to the client for as long as you want. - Survives proxies, firewalls, and CDNs that randomly kill WebSockets. What can you do with SSE? - Real-time dashboards: Live metrics, analytics, and data feeds with zero polling. - Live notifications: Push alerts and messages straight to the UI. - Activity feeds: Social posts, order tracking, delivery status, instant updates. - Progressive API responses: Stream search results, large data exports, or report lines as soon as they’re ready, instead of waiting for the whole payload. - Long-running job updates: CI/CD logs, deployment status, or batch jobs, show users status and output live. - Live system monitoring: Tail logs, monitor CPU/memory/network stats in real time. - IoT/sensor data: Push measurements directly as they happen. - Collaboration: Live presence, editing indicators, or comment notifications. - Live auctions/bidding: Broadcast bid changes, status, and time left. Don’t believe me? Try the attached code snippet, and see how easy it is. - No polling.  - No heavy protocols. - No maintenance nightmares. Just one HTTP connection, and the server pushes updates as they happen. Stop using WebSockets and AJAX for problems they were never designed to solve. Use the right tool for real-time: Server-Sent Events.

🚨 Just Published: Active Directory Security Event Monitoring - 41-Page Advanced Threat Detection Guide (Free PDF) "90% of Fortune 1000 companies run Active Directory. A single AD compromise = complete enterprise control." After years of detecting sophisticated AD attacks, I've documented everything about Active Directory security event monitoring in this comprehensive 41-page technical guide. The harsh reality: - Active Directory is the crown jewel target for APTs - Golden Ticket attacks can grant unlimited domain access for years - DCSync enables credential theft from any account in the domain - Most security teams can't detect Kerberoasting until it's too late - Average AD breach goes undetected because teams don't monitor the right events What I've packed into this guide: 🎟️ GOLDEN TICKET DETECTION → Behavioral analysis techniques → Service ticket anomaly detection → TGT lifetime monitoring → Production-ready PowerShell detection scripts 🔄 DCSYNC ATTACK DETECTION → Replication rights abuse monitoring → Non-DC replication attempt detection → Directory Service Access (Event 4662) correlation → Automated alerting frameworks 🎯 KERBEROASTING DETECTION → RC4 encryption usage patterns → Excessive service ticket request monitoring → Vulnerable service account identification → SPN security hardening 🔐 KERBEROS & AUTHENTICATION → Complete Kerberos event analysis (4768, 4769, 4770, 4771) → Password spray detection algorithms → After-hours authentication monitoring → NTLM downgrade attack detection 📊 LDAP & DIRECTORY MONITORING → Enumeration attempt detection → Sensitive attribute query monitoring → Bulk modification detection → LDAP injection prevention 🛡️ GROUP POLICY SECURITY → GPO modification detection → SYSVOL integrity monitoring → Suspicious file detection in GPOs → Unauthorized policy change alerting 🤖 MACHINE LEARNING DETECTION → Python-based anomaly detection framework → Behavioral baseline training → Feature extraction from AD events → Automated threat severity scoring ⚡ SIEM INTEGRATION → Production Splunk correlation rules → Elasticsearch Watcher configurations → Real-time alerting mechanisms → Cross-system event correlation 📜 REAL PRODUCTION CODE → PowerShell detection frameworks → Python ML implementation → Parallel event processing scripts → Forensic evidence collection procedures Why I wrote this: Tired of seeing enterprises get compromised through their AD Wanted to share the exact detection techniques I use in real investigations Created a comprehensive resource beyond basic "check Event Viewer" advice Documented the advanced attacks that most security teams miss 🎯 Want the complete 41-page guide with all detection scripts and SIEM rules? Drop a 🔐 below or DM me! #ActiveDirectory #CyberSecurity #ThreatDetection #SOC #IncidentResponse #SIEM #ThreatHunting #SecurityMonitoring #EnterpriseSecurty #Kerberos #ADSecurity #SecurityEngineering #BlueTeam #DFIR #InfoSec

Edge AI shifts from slide-deck fantasy to tactical reality. Rifle companies now carry 1,800 TOPS of computing power in two Pelican cases, enabling sense-decide-act cycles in 300 milliseconds even when every communication link is jammed or burning. Dell's new Pro Max systems with GB10 Grace-Blackwell accelerators fundamentally rewrite the tactical playbook. A 15-kilogram package now delivers what previously required containerized server farms, running 120-billion parameter models on vehicle power or 8-hour batteries without throttling. The operational specifications that matter include 1 petaFLOP of AI performance at 250W total system draw, and 128 GB of coherent unified LPDDR5x memory, keeping multiple models resident. NVIDIA Confidential Computing with FIPS 140-3 options for TS/SCI weights, while liquid-metal and vapor-chamber cooling enable 60°C ambient operation. This translates to AI firepower that survives Iraqi summers in a JLTV. Ukraine validates these capabilities daily. Pro Max Mini systems control 80 drones from a single Pelican case, using 120B coordination models that retask swarms, handle attrition, and adapt to jamming in real time. Marine Littoral Regiments are running models four times larger than 2024 systems at half the power consumption. The impact on engagement timelines is dramatic. Mast cameras stream 8K video at 120–180+ fps on 4K/8K feeds for instant threat detection. The system generates threat pop-up boxes, weapon slew commands, and shoot/don't shoot recommendations at 95% confidence levels. Human reaction time averages 4-6 seconds. AI-assisted response collapses to 400 milliseconds. Three fundamental shifts emerge from this edge computing revolution. Speed beats latency; 300ms local processing outperforms 3-second satellite round-trips. Resilience beats connectivity; systems function when everything else is offline. Integration beats isolation. One system handles every AI mission. For contractors: edge AI has moved from PowerPoint promises to operational reality. The question isn't whether to integrate AI at the tactical edge, but how fast you can deliver it. #DellProMax #EdgeAI #Defense